Detection of Autonomous Shuttles in Urban Traffic Images Using Adaptive Residual Context

TL;DR

This paper presents the Adaptive Residual Context (ARC) architecture, a novel method for detecting autonomous shuttles in urban traffic images that maintains scene understanding while learning new detection tasks efficiently.

Contribution

The ARC architecture introduces a new way to add detection capabilities without catastrophic forgetting, improving data efficiency and scene context preservation in urban traffic monitoring.

Findings

ARC matches fine-tuned baselines in detection accuracy.

ARC significantly improves knowledge retention during training.

The method is effective for complex urban environments.

Abstract

The progressive automation of transport promises to enhance safety and sustainability through shared mobility. Like other vehicles and road users, and even more so for such a new technology, it requires monitoring to understand how it interacts in traffic and to evaluate its safety. This can be done with fixed cameras and video object detection. However, the addition of new detection targets generally requires a fine-tuning approach for regular detection methods. Unfortunately, this implementation strategy will lead to a phenomenon known as catastrophic forgetting, which causes a degradation in scene understanding. In road safety applications, preserving contextual scene knowledge is of the utmost importance for protecting road users. We introduce the Adaptive Residual Context (ARC) architecture to address this. ARC links a frozen context branch and trainable task-specific branches through a Context-Guided Bridge, utilizing attention to transfer spatial features while preserving pre-trained representations. Experiments on a custom dataset show that ARC matches fine-tuned baselines while significantly improving knowledge retention, offering a data-efficient solution to add new vehicle categories for complex urban environments.